/**************************************************************************** * * Copyright (c) 2015-2023 PX4 Development Team. All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * 1. Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * 2. Redistributions in binary form must reproduce the above copyright * notice, this list of conditions and the following disclaimer in * the documentation and/or other materials provided with the * distribution. * 3. Neither the name PX4 nor the names of its contributors may be * used to endorse or promote products derived from this software * without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS * OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED * AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. * ****************************************************************************/ /** * @file terrain_estimator.cpp * Function for fusing rangefinder and optical flow measurements * to estimate terrain vertical position */ #include "ekf.h" #include "python/ekf_derivation/generated/terr_est_compute_flow_xy_innov_var_and_hx.h" #include "python/ekf_derivation/generated/terr_est_compute_flow_y_innov_var_and_h.h" #include void Ekf::initHagl() { #if defined(CONFIG_EKF2_OPTICAL_FLOW) stopHaglFlowFusion(); #endif // CONFIG_EKF2_OPTICAL_FLOW stopHaglRngFusion(); // assume a ground clearance _terrain_vpos = _state.pos(2) + _params.rng_gnd_clearance; // use the ground clearance value as our uncertainty _terrain_var = sq(_params.rng_gnd_clearance); } void Ekf::runTerrainEstimator(const imuSample &imu_delayed) { // If we are on ground, store the local position and time to use as a reference if (!_control_status.flags.in_air) { _last_on_ground_posD = _state.pos(2); _control_status.flags.rng_fault = false; } predictHagl(imu_delayed); controlHaglRngFusion(); #if defined(CONFIG_EKF2_OPTICAL_FLOW) controlHaglFlowFusion(); #endif // CONFIG_EKF2_OPTICAL_FLOW controlHaglFakeFusion(); // constrain _terrain_vpos to be a minimum of _params.rng_gnd_clearance larger than _state.pos(2) if (_terrain_vpos - _state.pos(2) < _params.rng_gnd_clearance) { _terrain_vpos = _params.rng_gnd_clearance + _state.pos(2); } } void Ekf::predictHagl(const imuSample &imu_delayed) { // predict the state variance growth where the state is the vertical position of the terrain underneath the vehicle // process noise due to errors in vehicle height estimate _terrain_var += sq(imu_delayed.delta_vel_dt * _params.terrain_p_noise); // process noise due to terrain gradient _terrain_var += sq(imu_delayed.delta_vel_dt * _params.terrain_gradient) * (sq(_state.vel(0)) + sq(_state.vel(1))); // limit the variance to prevent it becoming badly conditioned _terrain_var = math::constrain(_terrain_var, 0.0f, 1e4f); } void Ekf::controlHaglRngFusion() { if (!(_params.terrain_fusion_mode & TerrainFusionMask::TerrainFuseRangeFinder) || _control_status.flags.rng_fault) { stopHaglRngFusion(); return; } if (_range_sensor.isDataHealthy()) { const bool continuing_conditions_passing = _control_status.flags.in_air && _rng_consistency_check.isKinematicallyConsistent(); //const bool continuing_conditions_passing = _control_status.flags.in_air && !_control_status.flags.rng_hgt; // TODO: should not be fused when using range height const bool starting_conditions_passing = continuing_conditions_passing && _range_sensor.isRegularlySendingData() && (_rng_consistency_check.getTestRatio() < 1.f); _time_last_healthy_rng_data = _time_delayed_us; if (_hagl_sensor_status.flags.range_finder) { if (continuing_conditions_passing) { fuseHaglRng(); // We have been rejecting range data for too long const uint64_t timeout = static_cast(_params.terrain_timeout * 1e6f); const bool is_fusion_failing = isTimedOut(_time_last_hagl_fuse, timeout); if (is_fusion_failing) { if (_range_sensor.getDistBottom() > 2.f * _params.rng_gnd_clearance) { // Data seems good, attempt a reset resetHaglRng(); } else if (starting_conditions_passing) { // The sensor can probably not detect the ground properly // declare the sensor faulty and stop the fusion _control_status.flags.rng_fault = true; _range_sensor.setFaulty(true); stopHaglRngFusion(); } else { // This could be a temporary issue, stop the fusion without declaring the sensor faulty stopHaglRngFusion(); } } } else { stopHaglRngFusion(); } } else { if (starting_conditions_passing) { startHaglRngFusion(); } } } else if (_hagl_sensor_status.flags.range_finder && isTimedOut(_time_last_healthy_rng_data, _params.reset_timeout_max)) { // No data anymore. Stop until it comes back. stopHaglRngFusion(); } } void Ekf::startHaglRngFusion() { _hagl_sensor_status.flags.range_finder = true; resetHaglRngIfNeeded(); } void Ekf::resetHaglRngIfNeeded() { if (_hagl_sensor_status.flags.flow) { const float meas_hagl = _range_sensor.getDistBottom(); const float pred_hagl = _terrain_vpos - _state.pos(2); const float hagl_innov = pred_hagl - meas_hagl; const float obs_variance = getRngVar(); const float hagl_innov_var = fmaxf(_terrain_var + obs_variance, obs_variance); const float gate_size = fmaxf(_params.range_innov_gate, 1.0f); const float hagl_test_ratio = sq(hagl_innov) / (sq(gate_size) * hagl_innov_var); // Reset the state to the measurement only if the test ratio is large, // otherwise let it converge through the fusion if (hagl_test_ratio > 0.2f) { resetHaglRng(); } else { fuseHaglRng(); } } else { resetHaglRng(); } } float Ekf::getRngVar() { return fmaxf(P(9, 9) * _params.vehicle_variance_scaler, 0.0f) + sq(_params.range_noise) + sq(_params.range_noise_scaler * _range_sensor.getRange()); } void Ekf::resetHaglRng() { _terrain_vpos = _state.pos(2) + _range_sensor.getDistBottom(); _terrain_var = getRngVar(); _terrain_vpos_reset_counter++; _time_last_hagl_fuse = _time_delayed_us; _time_last_healthy_rng_data = 0; } void Ekf::stopHaglRngFusion() { if (_hagl_sensor_status.flags.range_finder) { _hagl_innov = 0.f; _hagl_innov_var = 0.f; _hagl_test_ratio = 0.f; _innov_check_fail_status.flags.reject_hagl = false; _hagl_sensor_status.flags.range_finder = false; } _time_last_healthy_rng_data = 0; } void Ekf::fuseHaglRng() { // get a height above ground measurement from the range finder assuming a flat earth const float meas_hagl = _range_sensor.getDistBottom(); // predict the hagl from the vehicle position and terrain height const float pred_hagl = _terrain_vpos - _state.pos(2); // calculate the innovation _hagl_innov = pred_hagl - meas_hagl; // calculate the observation variance adding the variance of the vehicles own height uncertainty const float obs_variance = getRngVar(); // calculate the innovation variance - limiting it to prevent a badly conditioned fusion _hagl_innov_var = fmaxf(_terrain_var + obs_variance, obs_variance); // perform an innovation consistency check and only fuse data if it passes const float gate_size = fmaxf(_params.range_innov_gate, 1.0f); _hagl_test_ratio = sq(_hagl_innov) / (sq(gate_size) * _hagl_innov_var); if (_hagl_test_ratio <= 1.0f) { // calculate the Kalman gain const float gain = _terrain_var / _hagl_innov_var; // correct the state _terrain_vpos -= gain * _hagl_innov; // correct the variance _terrain_var = fmaxf(_terrain_var * (1.0f - gain), 0.0f); // record last successful fusion event _time_last_hagl_fuse = _time_delayed_us; _innov_check_fail_status.flags.reject_hagl = false; } else { _innov_check_fail_status.flags.reject_hagl = true; } } #if defined(CONFIG_EKF2_OPTICAL_FLOW) void Ekf::controlHaglFlowFusion() { if (!(_params.terrain_fusion_mode & TerrainFusionMask::TerrainFuseOpticalFlow)) { stopHaglFlowFusion(); return; } if (_flow_data_ready) { updateOptFlow(_aid_src_terrain_optical_flow); const bool continuing_conditions_passing = _control_status.flags.in_air && !_control_status.flags.opt_flow && _control_status.flags.gps && isTimedOut(_time_last_hagl_fuse, 5e6f); // TODO: check for range_finder hagl aiding instead? /* && !_hagl_sensor_status.flags.range_finder; */ const bool starting_conditions_passing = continuing_conditions_passing; if (_hagl_sensor_status.flags.flow) { if (continuing_conditions_passing) { // TODO: wait until the midpoint of the flow sample has fallen behind the fusion time horizon fuseFlowForTerrain(_aid_src_terrain_optical_flow); _flow_data_ready = false; // TODO: do something when failing continuously the innovation check /* const bool is_fusion_failing = isTimedOut(_time_last_flow_terrain_fuse, _params.reset_timeout_max); */ /* if (is_fusion_failing) { */ /* resetHaglFlow(); */ /* } */ } else { stopHaglFlowFusion(); } } else { if (starting_conditions_passing) { startHaglFlowFusion(); } } } else if (_hagl_sensor_status.flags.flow && (_time_delayed_us > _flow_sample_delayed.time_us + (uint64_t)5e6)) { // No data anymore. Stop until it comes back. stopHaglFlowFusion(); } } void Ekf::startHaglFlowFusion() { _hagl_sensor_status.flags.flow = true; // TODO: do a reset instead of trying to fuse the data? fuseFlowForTerrain(_aid_src_terrain_optical_flow); _flow_data_ready = false; } void Ekf::stopHaglFlowFusion() { if (_hagl_sensor_status.flags.flow) { _hagl_sensor_status.flags.flow = false; resetEstimatorAidStatus(_aid_src_terrain_optical_flow); } } void Ekf::resetHaglFlow() { // TODO: use the flow data _terrain_vpos = fmaxf(0.0f, _state.pos(2)); _terrain_var = 100.0f; _terrain_vpos_reset_counter++; } void Ekf::fuseFlowForTerrain(estimator_aid_source2d_s &flow) { flow.fusion_enabled = true; const float R_LOS = flow.observation_variance[0]; // calculate the height above the ground of the optical flow camera. Since earth frame is NED // a positive offset in earth frame leads to a smaller height above the ground. float range = predictFlowRange(); const float state = _terrain_vpos; // linearize both axes using the same state value Vector2f innov_var; float H; sym::TerrEstComputeFlowXyInnovVarAndHx(state, _terrain_var, _state.quat_nominal, _state.vel, _state.pos(2), R_LOS, FLT_EPSILON, &innov_var, &H); innov_var.copyTo(flow.innovation_variance); if ((flow.innovation_variance[0] < R_LOS) || (flow.innovation_variance[1] < R_LOS)) { // we need to reinitialise the covariance matrix and abort this fusion step ECL_ERR("Opt flow error - covariance reset"); _terrain_var = 100.0f; return; } // run the innovation consistency check and record result setEstimatorAidStatusTestRatio(flow, math::max(_params.flow_innov_gate, 1.f)); _innov_check_fail_status.flags.reject_optflow_X = (flow.test_ratio[0] > 1.f); _innov_check_fail_status.flags.reject_optflow_Y = (flow.test_ratio[1] > 1.f); // if either axis fails we abort the fusion if (flow.innovation_rejected) { return; } // fuse observation axes sequentially for (uint8_t index = 0; index <= 1; index++) { if (index == 0) { // everything was already computed above } else if (index == 1) { // recalculate innovation variance because state covariances have changed due to previous fusion (linearise using the same initial state for all axes) sym::TerrEstComputeFlowYInnovVarAndH(state, _terrain_var, _state.quat_nominal, _state.vel, _state.pos(2), R_LOS, FLT_EPSILON, &flow.innovation_variance[1], &H); // recalculate the innovation using the updated state const Vector2f vel_body = predictFlowVelBody(); range = predictFlowRange(); flow.innovation[1] = (-vel_body(0) / range) - flow.observation[1]; if (flow.innovation_variance[1] < R_LOS) { // we need to reinitialise the covariance matrix and abort this fusion step ECL_ERR("Opt flow error - covariance reset"); _terrain_var = 100.0f; return; } } float Kfusion = _terrain_var * H / flow.innovation_variance[index]; _terrain_vpos += Kfusion * flow.innovation[0]; // constrain terrain to minimum allowed value and predict height above ground _terrain_vpos = fmaxf(_terrain_vpos, _params.rng_gnd_clearance + _state.pos(2)); // guard against negative variance _terrain_var = fmaxf(_terrain_var - Kfusion * H * _terrain_var, sq(0.01f)); } _fault_status.flags.bad_optflow_X = false; _fault_status.flags.bad_optflow_Y = false; _time_last_flow_terrain_fuse = _time_delayed_us; //_aid_src_optical_flow.time_last_fuse = _time_delayed_us; // TODO: separate aid source status for OF terrain? _aid_src_optical_flow.fused = true; } #endif // CONFIG_EKF2_OPTICAL_FLOW void Ekf::controlHaglFakeFusion() { if (!_control_status.flags.in_air && !_hagl_sensor_status.flags.range_finder && !_hagl_sensor_status.flags.flow) { initHagl(); } } bool Ekf::isTerrainEstimateValid() const { // we have been fusing range finder measurements in the last 5 seconds if (_hagl_sensor_status.flags.range_finder && isRecent(_time_last_hagl_fuse, (uint64_t)5e6)) { return true; } #if defined(CONFIG_EKF2_OPTICAL_FLOW) // we have been fusing optical flow measurements for terrain estimation within the last 5 seconds // this can only be the case if the main filter does not fuse optical flow if (_hagl_sensor_status.flags.flow && isRecent(_time_last_flow_terrain_fuse, (uint64_t)5e6)) { return true; } #endif // CONFIG_EKF2_OPTICAL_FLOW return false; }